1. Field of the Invention
The present invention relates generally to predicting consumer demand patterns in the retail industry, and, more particularly, to the long-range prediction of weather impact on the retail industry.
2. Related Art
I. The Evolution of Retail Industry Problems
The retail industry has experienced a rapid expansion of consumer demand in the last two decades. This increase in demand has enabled a large number of retailers to grow at the local, regional, and national levels. To meet this growth in consumer demand, most larger national retailers implemented a strategy of store expansion, i.e., increasing the square footage of retail space dedicated to selling products. This strategy, which was particularly common in the early to mid-1980's, resulted in huge increases in the number of stores under operation by a majority of major retailers. Though the increase in square footage enabled the retailers to meet consumer demand, it became difficult to manage the large number of decentralized stores.
These problems were further complicated in the late 1980's and early 1990's. Competition among retailers increased during this period due to recession, personal debt, and maturation of consumer demand. This forced retailers to change their approach from a growth strategy, which solely relied on store expansion, to a productivity-based strategy to maximize return from existing square footage. Essentially, the productivity-based strategy requires the maximum amount of product to be moved through a store at a minimum markdown. Implementation of this strategy resulted in planning and coordination problems to achieve the desired productivity at all levels, national, regional, and local.
The problems resulting from these two strategies are best described with reference to the five functions of the retail industry which are critical to the success of every retailer: buying, distributing, promotion, advertising and financial budgeting. These five functions are of paramount importance to large retailers due to the magnitude of items and locations required to sell directly to consumers.
These five functions are as follows. Buying is the procurement of a product based on an anticipated volume of consumer demand for that product. Distribution is the allocation of the product to the correct locations at the correct times to meet this anticipated consumer demand. Promotion is the offering of an inducement, such as a markdown, to prompt customers to visit a store and purchase specific products. Advertising is the act of selecting and utilizing media to implement promotions, as well as create and foster a desired consumer image for products and the company. Financial budgeting is the act of projecting and authorizing funds expenditures for all aspects of retailer operations to control and maximize financial performance.
Retailers have always been faced with the problem of coordinating the buying, distribution, promotion, advertising and budgeting activities with constantly changing consumer demand behavior. The magnitude of this problem increased dramatically with the rapid growth of multi-store retailers through additional square footage and the subsequent need to increase the productivity of each of these stores. Productivity-based growth strategies created a coordination requirement for accurate buying, distribution, promotion, advertising and budgeting at regional (merchandising region comprised of several states), MSA (metropolitan statistical area) and local (store location) levels. However, all of these functions historically have been controlled in a centralized fashion by managers based at headquarters.
II. Use of MIS Systems For Retail Sales Forecasting
The preferred approach to achieving this centralized control over a decentralized problem has been the development of computer based MIS (management information system). In the retail industry, the primary function of an MIS is the electronic collection, storage, retrieval, and analysis of data. By definition, retailers sell product to the consumer for profit. Naturally, any type of transaction in support of consumer sales activities is collected and flows through the MIS. Note that the term "transaction" is used broadly to represent any type of recordable event taking place in support of consumer sales (i.e., inventory transfer from distribution center to store, promotion data, store traffic, etc), not merely the time, amount and merchandise of a specific sale.
Retailers were initially forced to use mainframe-based MIS systems to store and manipulate data, simply due to the requisite storage and speed of processing provided by mainframe computers. Since understanding of local, MSA and region level dynamics is a requisite for increased retailing productivity, retailers would essentially feed POS (point-of-sale) transactions data at the store level into massive mainframe databases for subsequent analysis to identify basic trends. However, the use of mainframes typically requires the expense of a large MIS department to process data and analysis requests, as well as the delay from the time of request to the actual execution. This structure prevented the MIS systems from becoming cost effective for use by executives in making daily decisions, who are typically not computer specialists and thus had to refer data requests to MIS specialists.
In response to the need for rapid executive interface to data for managerial plan preparation, a large industry developed in Executive Information Systems (EIS) that interfaces into the MIS mainframe or mid-range database but typically operates on personal computer workstation platforms. An EIS system is a computer-based means by which information and analysis can be accessed, created, packaged and/or delivered for use on demand by users who are non-technical in background. An EIS system performs specific managerial applications without extensive interaction with the user, which reduces or eliminates the need for computer software training and documentation.
Technical improvements in speed and storage capability of PCs have allowed this trend to take place, while most firms still maintain a mainframe or minicomputer architecture for basic POS data storage and processing. The planning applications have first been implemented at the national and/or regional levels for buying, distributing, advertising, promotional and financial budgeting, although the basic POS store transactions data flows from each store location. The basic underlying approach of current MIS planning solutions to provide centralized control is to retrieve and store POS (store level) data, aggregate it into historical databases, and manipulate the data into useful productivity-based Executive Information Systems (EIS) yielding basic time-series trends in demand at regional or national levels.
Referring now to FIG. 1, a block diagram of a typical MIS system architecture is illustrated. MIS architecture 102 is designed to capture transactions data, and electronically flow this data throughout the organization for managerial planning and control purposes.
At the point of sale (POS) 104 electronic scanners 108, registers 110, and other electronic scanning and data gathering devices record transactions. Store transactions data 116 is electronically transferred to the headquarters typically by modem or broadcasting means. In a typical retail application, there are multiple point of sale locations. In FIG. 1, point of sale 106 has scanners 112, registers 110, and other electronic scanning and data gathering devices to record transactions in a similar manner to point of sale 104. Point of sale 106 electronically transmits store transaction data 118 to the data storage and retrieval facility 120. The headquarters data storage and retrieval facility 120 receives the data using computer hardware 122 and software 124, which is subsequently used for managerial planning purposes.
For analytical purposes the data is retrieved from data storage and retrieval facility 120 into a data analyzer 126 for use in the preparation of the managerial plan. Retrieval of the data into the data analyzer 126 can be manually generated as indicated by line 134 through a custom request to MIS department personnel or, in the preferred mode, electronically generated as indicated by line 132 into a workstation 128 for immediate viewing and use in the preparation of the managerial plan 130.
Current planning applications software has substantially improved the control of large multi-store retailers over the critical aspects of the retail business (buying, distribution, advertising, promotion, financial budgeting). For example, drawing on the historical sales trend experience of specific merchandise categories at the POS, and factoring in economic and consumer research and forecasting, retailers are more adept at developing national unit sales forecasts for buying and regional allocation for distribution. If they have overstocked at any particular store, product is moved through the store using markdown and other promotional techniques. If retailers understock at a store, buffer inventories and creative supplier relationships such as just-in-time quickly move to replenish. On a national level, these systems have brought major improvements in efficiency and profitability.
Ultimately, however, the goal of most leading multi-store retailers is to enable the MIS applications to perform the analysis at the MSA and/or store level where the true power of the MIS application resides in giving competitive advantage. However, presently, the retail industry has only developed its use of the MIS systems to the point of planning on a regional basis with anywhere from 5-20 merchandise regions. The present management structures and culture are gradually adjusting from the national to the more specific regionality in their decision-making process. This slow transition in the use of data inhibits the transition from national-to-regional-to-MSA-to-local analysis.
III. Weather Forecasting
Most retailers acknowledge weather as a critical variable to sales demand but have had little interest in, or means to address, weather impact from a planning perspective. Relative to weather issues specifically, MIS planning applications systems (both custom and packaged) have virtually ignored this planning variable, at least until very recently. This is partially due to the majority of national retailers only having a fully integrated POS MIS system operational for 3 to 5 years. This is also due to the slow transition in the use of data from the national level data to the MSA and store level.
A. The Nature of Weather Anomalies
Weather anomalies are more of a regional and local phenomenon rather than a national phenomenon. This is not to say that major anomalies cannot sweep an entire country or continent, creating abnormally hot or cold seasons, but they are less frequent than regional or local aberrations. Major precipitation and temperature anomalies occur continually on daily, weekly and monthly intervals in specific regions, MSAs or locations throughout the United States.
Another key point to consider about weather is that actual daily, weekly and monthly occurrences fluctuate greatly around the long term "normal" or "average" (in meteorology, normal is typically defined as a 30 year) trend line. In other words, past historical averages are a very poor predictor of future weather on a given day, week or month. Implicitly, weather effects are already embedded in an MIS POS database, so the retailer is consciously or unconsciously using some type of historical weather average as a factor in any planning approach that uses a trendline forecasts based on historical POS data for a given location and time period.
B. Weather Relative to National Planning Applications
At a national level, weather is only one of several important variables driving consumer demand for a retailer's products. Several obvious and usually more important factors are, for example, price, competition, quality, gross national product (GNP) trends, advertising exposure, and structure of the retailer's operations (number of stores, square footage, locations, etc). Relative to the national and regional implementation of planning, the impact of these other variables dominates trendline projections.
As described above, POS databases track sales trends of specific categories at specific locations which are then aggregated and manipulated into regional and national executive information reports. Since local and MSA weather anomalies can average out when aggregated to the national levels, the impact of weather has not received much scrutiny relative to national planning and forecasting. Weather occasionally creates dramatic increases or decreases in product demand on a national level but this is more of an exception as opposed to the rule. (In product manufacturing, this is not the case; weather often creates dramatic gains and losses for highly weather impacted manufacturers, such as air conditioners.)
IV. Weather Relative to Regional and Local Planning Applications
The impact of weather on a regional, MSA and local level is direct and dramatic. At a store level, weather is often the key driver of sales of specific product categories, and also influences store traffic which impacts sales of all goods. Weather directly influences timing and intensity of markdowns, and can create stockout situations which replenishment cycles can not address due to the inherent time lag of many replenishment approaches.
The combination of lost sales due to stockouts and markdowns required to move slow inventory, are enormous hidden costs, both in terms of lost income and opportunity costs. Aggregate these costs on a national level, and weather is one of the last major areas of retailing where costs can be carved out (eliminate overstocks) and stores can improve productivity (less markdown = more margin with same square footage).
Industry market research indicates that on average, many mass retailers operate with only a 50-60% fill rate, meaning approximately 50% of the time a customer in a store cannot find the desired product in stock. A one percent improvement in this fill rate can provide large improvement to the operating profits of a national retailer.
In short, weather can create windows of opportunity or potential pitfalls that are completely independent events relative to economics, consumer income, and competitive issues (price, quality). The cash and opportunity costs in the aggregate are enormous. Presently, the centralization of decision making has generally masked the importance of weather as a critical variable to retailing performance, as sharp local sales fluctuations due to weather tend to average out when aggregated into national numbers.
V. Conventional Solutions
Though the majority of retailers acknowledge the effects of weather, many do not consider weather as a problem per se, because they view it as a completely unpredictable part of the external environment, something that "everyone in the business lives with."
However, the underlying problem is in essence one of prediction of the future; developing a predictive model. Everyone in retail must forecast (informally or formally) how much inventory to buy and distribute based on expected demand and appropriate inventory buffers. Since weather is a critical driver of consumer demand for seasonal items, weather is part of this broader predictive modelling process that all retailers go through. Hence many conventional solutions exist to the overall predictive modelling process, none of which adequately addresses weather impact.
One conventional solution is not to consider the impact of weather. In such instances, the retailer will maintain high inventory levels and rapidly replenish the inventory as it is sold. This approach creates high working capital to support such a large inventory.
Another conventional solution is for the retailer to use past weather patterns to anticipate future consumer demands. This qualitative insight by decisionmakers has been proven to be inaccurate, subjective, and lacks regionalization. In addition, this method is not evaluating weather in a predictive sense.
Another conventional solution is the use of macroeconomic models. These methods typically lack regional specificity, only use historical weather data (typically long term "normals") which is a poor predictor, and typically do not tie into predictive models utilizing POS store transactions data 116, 118 (see FIG. 1). Since these models are not intended for regional and local applications, they are generally regarded as having poor accuracy on these levels. In addition, this approach does not have the one week forecast specificity required for retail planning, the typical retail planning increment of time.
Another conventional approach is the utilization of broad climatology forecasts. Manufacturers and retailers have been known to rely on broad projections developed by the National Weather Bureau (the governmental entity in the U.S.A.) and other private forecasting firms. These projections are generally acknowledged as being vague, broad projections, usually several sentences or paragraphs long, and of questionable accuracy. They completely lack the requisite regional or local specificity as well as the one week time increment, usually issued on a 30, 60 or 90 day basis. Also, these projections are not quantitative and therefore cannot easily integrate with an MIS-based planning system.
Another conventional approach is to address seasonality in databases. Many retailers graph basic seasonality curves based on POS data, calculating an average rate of sales and a seasonality value for any given week. This approach does not address weather specifically, but it does directly address seasonality at a national, regional, and local (store) level by calculating deseasonalized demand indexes (average rate of sales) and seasonality indices. While this approach has the requisite geographic specificity, it still has the same problem of relying on a historical average weather to determine the future weather impact. Regional, MSA and local weather fluctuates greatly around any type of historical "normal," rendering such projections invalid. One popular mainframe applications package which uses this approach is INFOREM, manufactured by International Business Machines, Poughkeepsie, N.Y., U.S.A.
Another conventional approach is to utilize short (1-3 day) forecasts from the National Weather Bureau or private forecasting service to qualitatively or quantitatively adjust decisions. These services typically issue weather reports for a nominal fee to commerce and industry. These reports are fairly accurate 1-3 days in advance for specific regions and MSAs. The accuracy drops off very quickly, and beyond 5-7 days, the regional specificity also broadens dramatically. The National Weather Bureau also issues very broad 30, 60 and 90 projections of limited commercial value. Forecasting which are limited to 1-3 days in the future do not serve the planning applications which require weeks, months or a even year of leadtime.
Another conventional solution is the use of multiple regression correlation. The technique of applying the least squares multiple regression algorithm to weather and sales datasets is approximately 30 years old. Some conventional approaches which use a form of this technique simply measure the weather impact from a market research perspective, and do not couple the correlation with forecasts for a predictive planning purpose. Use of this technique and others have been used in the utilities industry for 10-20 years to weather normalize results in a regulated public utility environment (weather accounts for 95%+ of variability in energy demand for regulated applications). Any consumption projections based on forecasts only look at 1-3 days in advance.
In the United Kingdom (UK), The Weather Initiative Ltd. (TWI), a division of the MET Office (England's equivalent of the United States National Weather Bureau) provides a correlation service coupled with short term (1-3 day) forecasting to adjust short term sales forecasts. TWI asserts that it has incorporated this concept into a software package for the MURCO Inc. stocking/distribution MIS system which is sold in the UK by Thorn/EMI Inc.
In summary, the above conventional solutions to weather planning problems in retail all suffer from one or several deficiencies which severely limit their commercial value, by not providing: (1) regional and/or local specificity in measuring past weather impact and projecting future weather impact, (2) the one week time increment of planning and forecasting required in the retail industry, (3) ample forecast leadtime, (anywhere from 1 week to 15 months), required by such planning applications as buying, advertising, promotion, distribution, and financial budgeting, (4) the quantification of weather impact required for precise planning applications such as unit buying and unit distribution and financial budget forecasting, (5) accuracy beyond a 3 day leadtime, (6) a predictive weather impact model, which links quantitative weather impact measurement through historical correlation, with quantitative, weekly forecasts, (7) an entirely electronic, computerized, EIS implementation for ease of data retrieval/analysis with specific functions that solve specific managerial planning applications 206-214 (EIS embodiment is a requirement for retail industry planners due to the lack of meteorological familiarity and the huge data manipulations) and (8) a graphical user interface represents the predictive model in graphs, formats, and charts immediately useful to the specific managerial applications of buying 206, advertising 210, distributing 208, promoting 212, and financial budgeting 214. By way of example, the graphical user interface provides a powerful screen which tells the user exactly when, where, and what to advertise versus a less useful numerical index.
What is needed is a Long-range Weather Executive Information System(LEWIS), containing a weather predictive model which provides specificity for both the location (MSA or store level) and time increment (one week). The forecast must be available early enough(1 week to 15 months in advance) to provide the necessary lead time for retail planners to respond to the data, and must be accurate. The LEWIS must interface to the present MIS system, and represent the analysis quickly and in a form which is tailored to the specific planning applications of the retail manager.